MS in Electrical Engineering or related field, or BS with equivalent hands-on experience
5+ years in RF geolocation, direction finding, or related signal processing
Experience in implementing angle-of-arrival (AoA) or time-difference-of-arrival (TDOA) techniques
Proficient in developing algorithms for validation of localization performance through field measurements
Strong understanding of array signal processing and multichannel calibration
Experience with RF hardware, captured IQ data, and laboratory test equipment
Responsibilities
Own the RF localization and direction-finding architecture
Evaluate and select localization estimation approaches
Trade localization accuracy against various system constraints
Develop AoA algorithms using multichannel RF data
Build TDOA localization methods with precisely timestamped detections
Design field tests to validate algorithms in real indoor environments
Analyze performance and feed findings back into system architecture
Benefits
Opportunity to work on cutting-edge RF sensing technology
Be part of a growing early-stage company
Impactful role with responsibility for a new localization capability
Collaborative environment with cross-functional teamwork
Potential for career growth in a high-demand field
Full Job Description
Senior RF Geolocation & Direction Finding Engineer - Build Indoor RF Localization from the Ground Up
Tampa, FL | On-site
Opportunity Summary
Join an early-stage defense technology company building advanced RF sensing and wireless systems for demanding operational environments. The team develops its own RF hardware, firmware, embedded systems, and software, and is now adding a new localization capability that must determine where wireless emitters are operating inside complex indoor environments.
You will own that capability from architecture through field validation. The technical challenge is difficult by design: concrete and steel, severe multipath, limited node density, intermittent transmitters, and multiple wireless protocols operating at once. You will determine the right combination of angle-of-arrival, time-difference-of-arrival, and sensor fusion, then prove the approach using real hardware in real buildings.
Job Duties
Own the RF localization and direction-finding architecture for a distributed sensing system
Evaluate and select between angle-of-arrival, time-difference-of-arrival, hybrid localization, and other estimation approaches
Trade localization accuracy against node count, antenna complexity, compute requirements, system cost, and deployment constraints
Develop AoA algorithms using phase-coherent, multichannel RF data
Build TDOA and cross-node localization methods using precisely timestamped detections
Develop sensor-fusion approaches that combine measurements from multiple nodes and localization techniques
Create timing, synchronization, and calibration error budgets and translate them into system and hardware requirements
Define channel-to-channel phase and amplitude calibration requirements for antenna arrays and coherent receiver paths
Work closely with RF engineers to define antenna configuration, array geometry, channel count, and receiver requirements
Partner with FPGA and DSP engineers to determine which signal-processing functions belong in FPGA fabric versus host or edge compute
Develop algorithms for multipath mitigation in dense indoor environments
Build tracking algorithms for moving, intermittent, and burst-mode emitters
Develop confidence metrics and uncertainty estimates around calculated device positions
Create simulation and playback environments using captured RF data before validating algorithms on live systems
Design and execute field tests in representative indoor facilities
Quantify localization accuracy, repeatability, coverage, and failure modes rather than relying only on simulation results
Analyze poor-performing scenarios and feed findings back into RF, timing, antenna, and system architecture
Support emitter detection and classification across cellular, Wi-Fi, Bluetooth, GNSS, and other wireless technologies
Help establish the company's long-term technical foundation for RF geolocation and direction finding
Qualifications
MS in Electrical Engineering, Applied Physics, Signal Processing, or a related technical field, or a BS with equivalent hands-on experience
5+ years of experience in RF geolocation, direction finding, wireless localization, radar, or closely related signal-processing work
Demonstrated experience implementing AoA, TDOA, or comparable localization techniques on real RF hardware
Experience validating localization performance through field measurements rather than simulation alone
Strong understanding of array signal processing and estimation theory
Practical knowledge of phase-coherent receiver architectures and multichannel calibration
Experience developing timing, synchronization, and measurement error budgets
Strong foundation in statistical estimation and target or emitter tracking
Hands-on experience addressing multipath, reflections, and non-line-of-sight propagation in indoor or urban environments
Proficiency with Python and C++
Experience using MATLAB or similar tools for algorithm development and prototyping
Ability to transition research or prototype algorithms into production-oriented implementations
Comfortable working directly with RF hardware, captured IQ data, antenna arrays, and laboratory test equipment
Ability to troubleshoot problems across algorithms, RF hardware, synchronization, calibration, and field conditions
Preferred Experience
Background in SIGINT, electronic warfare, direction finding, spectrum monitoring, or RF intelligence systems
Experience with UWB, RTLS, cellular localization, indoor positioning, or distributed sensor networks
Working knowledge of LTE, 2G, 3G, or 5G air interfaces
Familiarity with Wi-Fi and Bluetooth signal structures
Experience with precision timing technologies such as PTP, White Rabbit, GPS-disciplined oscillators, or equivalent synchronization systems
Strong understanding of how timing uncertainty translates into TDOA localization error
Experience with antenna arrays, beamforming, MUSIC, ESPRIT, or related direction-finding algorithms
Familiarity with Kalman filtering, particle filtering, Bayesian estimation, or other tracking approaches
Experience with RF emitter classification or machine-learning-based signal identification
Experience developing distributed localization systems that operate without constant communication between sensing nodes
Background in defense, public safety, telecommunications, wireless infrastructure, test and measurement, or other RF-intensive industries
